Systems and methods for predicting the trajectory of a road agent external to a vehicle

ABSTRACT

Systems and methods described herein relate to predicting a trajectory of a road agent external to a vehicle. One embodiment generates first and second predicted road-agent trajectories using respective first and second trajectory predictors based, at least in part, on a plurality of inputs including past road-agent trajectory information and vehicle sensor data; generates a confidence score for each predicted road-agent trajectory using a confidence estimator that includes a deep neural network, wherein generating the confidence scores includes computing them as a function of time within a predetermined temporal horizon; outputs the first and second predicted road-agent trajectories and their respective confidence scores; and controls operation of the vehicle based, at least in part, on one or more of the first predicted road-agent trajectory, the second predicted road-agent trajectory, the confidence score for the first predicted road-agent trajectory, and the confidence score for the second predicted road-agent trajectory.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 62/731,895, “Uncertainty-Aware Driver TrajectoryPrediction at Urban Intersections,” filed Sep. 15, 2018, which isincorporated by reference herein in its entirety.

TECHNICAL FIELD

The subject matter described herein relates in general to vehicles and,more specifically, to systems and methods for predicting the trajectoryof a road agent external to a vehicle.

BACKGROUND

In some applications, systems may predict the trajectory of a road agentexternal to a vehicle. Examples of road agents include various types ofother vehicles (e.g., automobiles, motorcycles, or bicycles) andpedestrians. One objective for an autonomous vehicle or aparallel-autonomy vehicle whose control is shared between a human driverand an autonomous-driving system is to travel a route without collidingwith the road agents the vehicle encounters along the way. Since theintentions of road agents or their drivers are not usually known withcertainty to an autonomous vehicle or the driver of a parallel-autonomyvehicle, predicting the trajectory of a road agent can further thatobjective, but current trajectory prediction systems fail to accountadequately for the uncertainty surrounding the actions of road agents.

SUMMARY

An example of a system for predicting a trajectory of a road agent ispresented herein. The system comprises one or more processors and amemory communicably coupled to the one or more processors. The memorystores a trajectory-prediction module including instructions that whenexecuted by the one or more processors cause the one or more processorsto generate a first predicted road-agent trajectory using a firsttrajectory predictor, wherein the road agent is external to a vehicleand the first trajectory predictor generates the first predictedroad-agent trajectory based, at least in part, on a plurality of inputsthat include past road-agent trajectory information and information fromone or more sensors of the vehicle. The trajectory-prediction modulealso generates a second predicted road-agent trajectory using a secondtrajectory predictor, wherein the second trajectory predictor generatesthe second predicted road-agent trajectory based, at least in part, onthe plurality of inputs. The trajectory-prediction module also generatesa confidence score for each of the first and second predicted road-agenttrajectories using a confidence estimator that includes a first deepneural network, wherein the confidence scores are computed as a functionof time within a predetermined temporal horizon. The memory also storesa trajectory-output module including instructions that when executed bythe one or more processors cause the one or more processors to outputthe first and second predicted road-agent trajectories and theirrespective confidence scores. The memory also stores a control moduleincluding instructions that when executed by the one or more processorscause the one or more processors to control operation of the vehiclebased, at least in part, on one or more of the first predictedroad-agent trajectory, the second predicted road-agent trajectory, theconfidence score for the first predicted road-agent trajectory, and theconfidence score for the second predicted road-agent trajectory.

Another embodiment is a non-transitory computer-readable medium forpredicting a trajectory of a road agent and storing instructions thatwhen executed by one or more processors cause the one or more processorsto generate a first predicted road-agent trajectory using a firsttrajectory predictor, wherein the road agent is external to a vehicleand the first trajectory predictor generates the first predictedroad-agent trajectory based, at least in part, on a plurality of inputsthat include past road-agent trajectory information and information fromone or more sensors of the vehicle. The instructions also cause the oneor more processors to generate a second predicted road-agent trajectoryusing a second trajectory predictor, wherein the second trajectorypredictor generates the second predicted road-agent trajectory based, atleast in part, on the plurality of inputs. The instructions also causethe one or more processors to generate a confidence score for each ofthe first and second predicted road-agent trajectories using aconfidence estimator that includes a first deep neural network, whereinthe confidence scores are computed as a function of time within apredetermined temporal horizon. The instructions also cause the one ormore processors to output the first and second predicted road-agenttrajectories and their respective confidence scores and to controloperation of the vehicle based, at least in part, on one or more of thefirst predicted road-agent trajectory, the second predicted road-agenttrajectory, the confidence score for the first predicted road-agenttrajectory, and the confidence score for the second predicted road-agenttrajectory.

Another embodiment is a method of predicting a trajectory of a roadagent, the method comprising generating a first predicted road-agenttrajectory using a first trajectory predictor, wherein the road agent isexternal to a vehicle and the first trajectory predictor generates thefirst predicted road-agent trajectory based, at least in part, on aplurality of inputs that include past road-agent trajectory informationand information from one or more sensors of the vehicle. The method alsoincludes generating a second predicted road-agent trajectory using asecond trajectory predictor, wherein the second trajectory predictorgenerates the second predicted road-agent trajectory based, at least inpart, on the plurality of inputs. The method also includes generating aconfidence score for each of the first and second predicted road-agenttrajectories using a confidence estimator that includes a first deepneural network, wherein generating the confidence scores includescomputing the confidence scores as a function of time within apredetermined temporal horizon. The method also includes outputting thefirst and second predicted road-agent trajectories and their respectiveconfidence scores and controlling operation of the vehicle based, atleast in part, on one or more of the first predicted road-agenttrajectory, the second predicted road-agent trajectory, the confidencescore for the first predicted road-agent trajectory, and the confidencescore for the second predicted road-agent trajectory.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various systems, methods, andother embodiments of the disclosure. It will be appreciated that theillustrated element boundaries (e.g., boxes, groups of boxes, or othershapes) in the figures represent one embodiment of the boundaries. Insome embodiments, one element may be designed as multiple elements ormultiple elements may be designed as one element. In some embodiments,an element shown as an internal component of another element may beimplemented as an external component and vice versa. Furthermore,elements may not be drawn to scale.

FIG. 1 illustrates one embodiment of a vehicle within which systems andmethods disclosed herein may be implemented.

FIG. 2 illustrates one embodiment of a trajectory prediction system.

FIG. 3 illustrates one example of a situation in which predicting thetrajectory of a road agent might be desired.

FIG. 4 is a block diagram of a trajectory-prediction module, inaccordance with an illustrative embodiment of the invention.

FIG. 5 is a block diagram of a variational trajectory predictor, inaccordance with an illustrative embodiment of the invention.

FIG. 6 is a block diagram of a confidence estimator, in accordance withan illustrative embodiment of the invention.

FIG. 7 is a block diagram of a child network for image input data, inaccordance with an illustrative embodiment of the invention.

FIG. 8 is a block diagram of a child network for some other types ofinput data from vehicle sensors, in accordance with an illustrativeembodiment of the invention.

FIG. 9 is a flowchart of a method of predicting a trajectory of a roadagent, in accordance with an illustrative embodiment of the invention.

FIG. 10 is a flowchart of a method of training the deep neural networkof a variational trajectory predictor and the deep neural network of aconfidence estimator, in accordance with an illustrative embodiment ofthe invention.

DETAILED DESCRIPTION

The embodiments described herein address important weaknesses inexisting road-agent trajectory-prediction systems. Deterministictrajectory prediction algorithms can fail to adequately capture theuncertain nature of human actions, particularly the actions of a humandriver or pedestrian. Data-driven approaches to predicting thetrajectory of a road agent can learn common characteristics fromdatasets containing demonstrated trajectories, but those methods may notperform well in scenarios in which the road agent can choose from amongmultiple choices (e.g., turn left or proceed straight at anintersection).

The embodiments described herein overcome the above weaknesses by (1)employing multiple trajectory predictors simultaneously and (2)providing a confidence estimate for the predicted vehicle trajectoriesgenerated by the respective trajectory predictors so that theirtrustworthiness can be evaluated. An important aspect of the disclosedembodiments is the temporal (time) horizon over which a road agent'strajectory is predicted. For example, a given predicted trajectory froma particular trajectory predictor might be trustworthy over a relativelyshort temporal horizon of 0.1 to 3 seconds, but it might not betrustworthy over a longer temporal horizon extending beyond 3 seconds upto 10 seconds. In various embodiments, the confidence estimates for therespective road-agent trajectory predictions from the multipletrajectory predictors are computed as a continuous-time function overthe applicable temporal horizon using a deep-neural-network (DNN) model.The confidence measures thus assist the trajectory prediction system indeciding which trajectory predictions are most trustworthy forparticular segments of the overall temporal prediction horizon.

The various embodiments described herein can make use of two or moretrajectory predictors, and those trajectory predictors can use differentdeterministic or probabilistic computational models. For example, in oneembodiment including two trajectory predictors, the first trajectorypredictor is a probabilistic variational trajectory predictor thatincludes a DNN, and the second trajectory predictor is a physics-based(deterministic) model. In various embodiments, the trajectory predictorsreceive, as inputs, any of a variety of vehicle sensor data such aslight detection and ranging (LIDAR) data, image data from one or morecameras, radar data, and sonar data. Other input data such as the linearvelocity and angular velocity of a road agent can be derived from ananalysis of the sensor data. Depending on the particular embodiment, thetrajectory predictors may also receive, as input, measured pasttrajectory information for one or more road agents.

In various embodiments, the predicted trajectories of a road agent andtheir associated confidence scores can be used to control, at least inpart, the operation of an autonomous or semi-autonomous vehicle. Forexample, the vehicle can plan a safe trajectory for itself based, atleast in part, on the most likely predicted trajectory of a road agent.In general, the techniques described herein can be applied to at leastthe following use cases: (1) driving safely without colliding with roadagents, when the vehicle is operating autonomously; and (2) gauging riskand determining an optimal trajectory, when the vehicle is operating ina parallel-autonomy mode (e.g., when a human driver is driving thevehicle with a driver-assistance system engaged).

In this description, the term “road agent” refers generally to anyobject that is capable of moving from place to place along or in amanner that intersects with a roadway. Such objects are not alwaysnecessarily in motion, however. For example, various embodimentsdescribed herein consider an automobile parked along a street to be aroad agent. In those embodiments, the system tracks the parkedautomobile, along with other detected objects in the environment, usingthe vehicle's sensors. The sensor data would reveal that the road agent(the parked automobile) is stationary—that there is no trajectoryassociated with it that can be predicted at that time. However, in thosevarious embodiments, the system might continue to track the parkedautomobile because it could begin moving at any time. In embodiments,the road agents of interest are external to a vehicle (sometimesreferred to herein as the “ego vehicle” or “host vehicle”) in which anembodiment of the invention is operating. Some examples of road agentsinclude, without limitation, other vehicles of various types(automobiles, motorcycles, bicycles, trucks, construction equipment,etc.), pedestrians, and animals.

Referring to FIG. 1, an example of a vehicle 100 is illustrated. As usedherein, a “vehicle” is any form of motorized transport. In one or moreimplementations, the vehicle 100 is an automobile. While arrangementswill be described herein with respect to automobiles, it will beunderstood that embodiments are not limited to automobiles. In someimplementations, the vehicle 100 may be any other form of motorizedtransport that, for example, can operate at least semi-autonomously.

The vehicle 100 also includes various elements. It will be understoodthat in various embodiments it may not be necessary for the vehicle 100to have all of the elements shown in FIG. 1. The vehicle 100 can haveany combination of the various elements shown in FIG. 1. Further, thevehicle 100 can have additional elements to those shown in FIG. 1. Insome arrangements, the vehicle 100 may be implemented without one ormore of the elements shown in FIG. 1. While the various elements areshown as being located within the vehicle 100 in FIG. 1, it will beunderstood that one or more of these elements can be located external tothe vehicle 100. Further, the elements shown may be physically separatedby large distances.

Some of the possible elements of the vehicle 100 are shown in FIG. 1 andwill be described along with subsequent figures. However, a descriptionof many of the elements in FIG. 1 will be provided after the discussionof FIGS. 2-10 for purposes of brevity of this description. Additionally,it will be appreciated that for simplicity and clarity of illustration,where appropriate, reference numerals have been repeated among thedifferent figures to indicate corresponding or analogous elements. Inaddition, the discussion outlines numerous specific details to provide athorough understanding of the embodiments described herein. Thoseskilled in the art, however, will understand that the embodimentsdescribed herein may be practiced using various combinations of theseelements.

The vehicle 100, sometimes referred to herein as the “ego vehicle” or“host vehicle,” includes a trajectory prediction system 170 that isimplemented to perform methods and other functions as disclosed hereinrelating to predicting a road agent's trajectory. In some embodiments,the road agent's trajectory is modeled in three-dimensional space.

With reference to FIG. 2, one embodiment of the trajectory predictionsystem 170 of FIG. 1 is further illustrated. The trajectory predictionsystem 170 is shown as including one or more processors 110 from thevehicle 100 of FIG. 1. Accordingly, the one or more processors 110 maybe a part of the trajectory prediction system 170, the trajectoryprediction system 170 may include one or more separate processors fromthe one or more processors 110 of the vehicle 100, or the trajectoryprediction system 170 may access the one or more processors 110 througha data bus or another communication path, depending on the embodiment.In one embodiment, the trajectory prediction system 170 includes amemory 210 that stores a trajectory-prediction module 220, atrajectory-output module 230, a control module 235, and a model-trainingmodule 240. The memory 210 is a random-access memory (RAM), read-onlymemory (ROM), a hard-disk drive, a flash memory, or other suitablememory for storing the modules 220, 230, 235, and 240. The modules 220,230, 235, and 240 are, for example, computer-readable instructions thatwhen executed by the one or more processors 110, cause the one or moreprocessors 110 to perform the various functions disclosed herein.

In connection with predicting the trajectory of one or more road agents,trajectory prediction system 170 can store various kinds ofmodel-related data 260 in database 250. As shown in FIG. 1, trajectoryprediction system 170 receives sensor data from sensor system 120. Forexample, in some embodiments, trajectory prediction system 170 receivesimage data from one or more cameras 126. Trajectory prediction system170 may also receive LIDAR data from LIDAR sensors 124, radar data fromradar sensors 123, and/or sonar data from sonar sensors 125, dependingon the particular embodiment. In some embodiments, trajectory predictionsystem 170 receives additional input data that is derived from ananalysis of the kinds of sensor data just mentioned. Examples of suchderived input data include the estimated linear velocity and/or angularvelocity of a road agent. As also indicated in FIG. 1, trajectoryprediction system 170, in particular control module 235, can communicatewith vehicle systems 140 to control, at least in part, certain aspectsof the operation of vehicle 100 such as steering system 143, brakingsystem 142, and throttle system 144, in some situations.

Trajectory-prediction module 220 generally includes instructions thatcause the one or more processors 110 to produce one or more trajectorypredictions pertaining to a road agent and, for each trajectoryprediction, a confidence estimate indicating the level of confidence orcertainty associated with that road-agent trajectory prediction. In someembodiments, trajectory-prediction module 220 bases its trajectorypredictions and corresponding confidence estimates on the measured pasttrajectory of the road agent and on one or more current sensor inputsfrom sensor system 120, as discussed above.

FIG. 3 illustrates one example of a situation in which predicting thetrajectory of a road agent might be desired. In FIG. 3, vehicle 100 (theego vehicle) and a road agent 305 are approaching an intersection 300from opposite directions. At intersection 300, road agent 305 (anotherautomobile) has at least three choices: (1) proceed straight (trajectory310 a); (2) turn right (trajectory 310 b); or (3) turn left (trajectory310 c). Predicting which of these three possible trajectories road agent305 is most likely to choose permits vehicle 100 to plan a safetrajectory based, at least in part, on that information. In someembodiments, trajectory-prediction module 220 produces, for the variouspossible road-agent trajectories, (1) probability distributionsconditioned on past road-agent trajectory and current sensor inputs fromsensor system 120 and (2) a confidence estimate for each predictedroad-agent trajectory. This information can be output to otherfunctional units of trajectory prediction system 170 such as controlmodule 235 via trajectory-output module 230, as explained in furtherdetail below.

The applicability of trajectory-prediction module 220 is not limited tointersections. Predicting the trajectory of a road agent can also applywhen vehicle 100 is traveling along a highway where there is nointersection. For example, on a highway, vehicle 100 can encountervehicles in adjacent lanes, vehicles traveling in the oppositedirection, pedestrians or animals darting onto the roadway, etc.

FIG. 4 is a block diagram of a trajectory-prediction module 220, inaccordance with an illustrative embodiment of the invention. In thisparticular embodiment, the inputs 410 are fed to a variationaltrajectory predictor 420, a confidence estimator 430, and one or moreadditional expert predictors 440, each of which produces an additionalexpert trajectory prediction 454. In general, there may be n additionalexpert predictors 440 in this mixture-of-experts architecture. In oneembodiment, one of these additional expert predictors 440 is aphysics-based (deterministic) model. As those skilled in the art areaware, a physics-based model analyzes the dynamics of a detected roadagent based on sensor data (image, LIDAR, etc.). Thus, as justillustrated, the trajectory predictors in trajectory-prediction module220 can employ different computational models, in some embodiments. Itshould be noted that, though FIG. 4, for simplicity, shows all of theinputs 410 being fed to each of the road-agent trajectory predictors, agiven trajectory predictor might not actually make use of all of theinputs 410, depending on the embodiment.

In the embodiment shown in FIG. 4, variational trajectory predictor 420includes a model that assumes future road-agent trajectories, projectedonto a polynomial basis, form a Gaussian mixture model (GMM) withdiagonal covariance matrices. Given a trajectory τ_(x)(t): [0, T]→

² and function basis B, the projection coefficients c_(x) can becomputed as c_(x)=Proj_(B)(τ), and the trajectory τ_(x) can be computedas τ_(x)=Bc. The bold typeface of certain variables indicates that theseare vector quantities. Analogous relationships apply to the trajectoryτ_(y) and projection coefficients c_(y). Thus, a probabilitydistribution over future trajectory can be transformed from a set ofprojection coefficients, and each projection coefficient is representedas a GMM. The number of components represents the distribution of thelikely movements of a detected road agent. For instance, using fourcomponents may yield two μ (mean) components that are nearly identical,and two other more distinct components. This would indicate that thereare three distinct likely trajectories for the road agent. The GMMparameters 450 produced by variational trajectory predictor 420 includethe weights w, the means μ_(x) and μ_(y), and the variances σ_(x) ² andσ_(y) ² of the projection coefficients associated with a futureroad-agent trajectory. The structure of variational trajectory predictor420 is discussed in greater detail below.

As will be seen below, confidence estimator 430 has a structure that issimilar to that of variational trajectory predictor 420. In theembodiment shown in FIG. 4, confidence estimator 430 outputs a set ofsecond-order polynomial coefficients that map the applicable predictivetemporal horizon to confidence scores (e.g., L2 prediction error,root-mean-squared error) for each candidate road-agent trajectorypredictor in trajectory-prediction module 220. In one embodiment, thepredictive temporal horizon is 0.1 to 3 seconds for L2 prediction error.Those skilled in the art will recognize that the L2 prediction error (orloss function) is the sum of the squared differences between the true ortarget values and the estimated values. In other embodiments, thetemporal horizon for predicting future trajectories may be longer (e.g.,ten seconds). In the embodiment shown in FIG. 4, the confidence scorescomputed by confidence estimator 430 are a continuous function of timewithin a predetermined temporal horizon. Though the inputs 410 includediscrete sampled data (e.g., camera images, LIDAR point-cloud data), theconfidence measure itself is a continuous-time function, meaning that itcan be computed for any time instant within the applicable temporalhorizon.

In some embodiments, mixture predictor 460 chooses the best (mostlikely) road-agent trajectory prediction among the trajectorypredictions produced by the variational trajectory predictor 420 and then additional expert predictors 440 based on their respective confidencescores from confidence estimator 430. That is, in these embodiments,mixture predictor 460 selects as the most likely predicted road-agenttrajectory the one having a confidence score 452 indicating the highestlevel of confidence among the candidate predicted road-agenttrajectories. In other embodiments, mixture predictor 460 passes along,to trajectory-output module 230, the road-agent trajectory predictionsproduced by the variational trajectory predictor 420 and the nadditional expert predictors 440 and their respective confidence scores452 without selecting a best road-agent trajectory prediction. In thoseembodiments, the road-agent trajectory predictions and their respectiveconfidence scores 452 produced by trajectory-prediction module 220(shown as trajectory predictions 470 in FIG. 4) are passed totrajectory-output module 230. As explained below, trajectory-outputmodule 230 includes instructions that cause the one or more processors110 to output the road-agent trajectory prediction or predictionsreceived from trajectory-prediction module 220. For example, theroad-agent trajectory prediction or predictions can be output to anotherfunctional unit of trajectory prediction system 170 such as controlmodule 235. If none of the predicted road-agent trajectories produced bythe trajectory predictors in trajectory-prediction module 220 is deemedtrustworthy, based on their respective confidence scores 452, mixturepredictor 460 can forward a warning to that effect to trajectory-outputmodule 230.

FIG. 5 is a block diagram of a variational trajectory predictor 420, inaccordance with an illustrative embodiment of the invention. In thisembodiment, variational trajectory predictor 420 includes a deep neuralnetwork (DNN) containing one or more child networks for each type ofinput data. As shown in FIG. 5, inputs 410 (refer to FIG. 4), in thisembodiment, include image data (from one or more cameras 126) 505, LIDARdata 510, past-trajectory data 515 (i.e., data pertaining to themeasured past trajectory of a road agent), estimated linear velocitydata 520 for the road agent, and estimated angular-velocity data 525 forthe road agent. As discussed above, the linear and angular velocity of aroad agent can be estimated based on sensor data from sensor system 120of vehicle 100.

In some embodiments, other or additional kinds of data from sensorsystem 120 can be fed to variational trajectory predictor 420, such asradar, and/or sonar data. Additionally, more highly structured data suchas a rasterized map (e.g., an occupancy grid for the environmentsurrounding vehicle 100) can be fed to variational trajectory predictor420, in some embodiments. Which specific kinds of raw sensor data orstructured data are fed to variational trajectory predictor 420 canvary, depending on the embodiment. In the embodiment shown in FIG. 4,image data 505 is fed to an images child network (“ImageNet”) 530, andLIDAR data 510 is fed to a (“LIDARNet”) 535. Past-trajectory data 515for the road agent is fed to a road-agent-dynamics child network(“DynamicsNet”) 540.

Past-trajectory data 515 includes a sequence of spatial coordinatesprojected onto one or more coefficients of a basis function, asdiscussed above in connection with the model for predictions of futureroad-agent trajectories. Linear-velocity data 520 for the road agent isfed to a linear-velocity child network (“VelNet”) 545. Angular-velocitydata 525 for the road agent is fed to an angular-velocity child network(“AngularNet”) 550. As mentioned above, a road agent's linear andangular velocity can be estimated from sensor data (image, LIDAR, radar,sonar, or a combination). The outputs of the child networks in theembodiment of FIG. 5 are collected in merged tensor 560. From the datacollected in merged tensor 560, predictor network (“PredictorNet”) 570calculates the GMM parameters 450 discussed above.

FIG. 6 is a block diagram of a confidence estimator 430, in accordancewith an illustrative embodiment of the invention. As mentioned above,confidence estimator 430, in this embodiment, has a structure similar tothat of variational trajectory predictor 420. That is, it includes itsown separate DNN with one or more child networks for each type of input.As shown in FIG. 6, confidence estimator 430 receives and processes theinputs 410 discussed in connection with FIG. 5. More specifically, imagedata 505 is fed to ImageNet 610, LIDAR data 510 is fed to LIDARNet 620,road-agent past-trajectory data 515 is fed to DynamicsNet 630,road-agent linear-velocity data 520 is fed to VelNet 640, and road-agentangular-velocity data 525 is fed to AngularNet 650. The outputs of thesechild networks are collected in merged tensor 660. Confidence network(“ConfidenceNet”) 670 computes the confidence scores 452 as a continuousfunction of time within the temporal horizon of the road-agenttrajectory predictions based on the data in merged tensor 660. Asdiscussed above in connection with variational trajectory predictor 420,in some embodiments, other types of sensor data such as radar and/orsonar may be fed to confidence estimator 430.

FIG. 7 is a block diagram of a child network for image input data(ImageNet 530), in accordance with an illustrative embodiment of theinvention. As shown in FIG. 7, in this particular embodiment, inputimage data (e.g., from one or more cameras 126) is processed through thefollowing series of stages/layers: 64-layer 3×3 convolutional processingstage 705, max pooling 2D layer 710, 128-layer 3×3 convolutionalprocessing stage 715, max pooling 2D layer 720, 256-layer 3×3convolutional processing stage 725, 256-layer 3×3 convolutionalprocessing stage 730, max pooling 2D layer 735, 512-layer 3×3convolutional processing stage 740, 512-layer 3×3 convolutionalprocessing stage 745, max pooling 2D layer 750, 512-layer 3×3convolutional processing stage 755, 512-layer 3×3 convolutionalprocessing stage 760, max pooling 2D layer 765, 4096-unit fullyconnected layer 770, 4096-unit fully connected layer 775, and 64-unitfully connected layer 780. As those skilled in the art are aware, max(maximum) pooling 2D is a technique for reducing the size of images bytaking the maximum value within 2×2-pixel image regions. In someembodiments, the output of ImageNet 530 can be fed to and accumulated ina long short-term memory (LSTM) network (not shown in FIG. 7).

FIG. 8 is a block diagram of a child network architecture that can beused for DynamicsNet 540, VelNet 545, or AngularNet 550 to process,respectively, road-agent past-trajectory data 515, road-agentlinear-velocity data 520, or road-agent angular-velocity data 525, inaccordance with an illustrative embodiment of the invention. As shown inFIG. 8, child network 800 is a fully connected neural network having twofully connected layers. In the particular example shown in FIG. 8, inputlayer 810 accepts two inputs. In other embodiments, child network 800accepts only one input. The input or inputs are fed to hidden layer 820,where each unit of the network is computed by multiplying each inputvalue by a weight 850 and summing the multiplications with a bias value840. Hidden layer 820 is called “hidden” because the values in thatlayer are not explicitly known, as those skilled in the art are aware.Output layer 830 has a structure similar to that of hidden layer 820,except that each unit in output layer 830 is connected to more units inthe previous hidden layer 820.

Referring again to FIG. 2, trajectory prediction system 170 alsoincludes trajectory-output module 230. As discussed above,trajectory-output module 230 receives one or more predictions of thefuture trajectory of a road agent and the respective confidence scores452 associated with those trajectory predictions fromtrajectory-prediction module 220 (shown as trajectory predictions 470 inFIG. 4). Trajectory-output module 230 outputs, to other functional unitsof trajectory prediction system 170 such as control module 235, one ormore predicted road-agent trajectories and their correspondingconfidence scores 452 received from trajectory-prediction module 220. Inone embodiment, trajectory-output module 230 outputs first and secondpredicted road-agent trajectories and their respective confidencescores. Generalizing, depending on the embodiment, trajectory-outputmodule 230 may output m predicted road-agent trajectories and theirassociated confidence scores 452, where m is greater than or equal totwo. In one embodiment, the first predicted road-agent trajectory isgenerated by variational trajectory predictor 420, and the secondpredicted road-agent trajectory is generated by an additional expertpredictor 440 employing a different computational model (e.g., aphysics-based model).

In a different embodiment, trajectory-output module 230 receives fromtrajectory-prediction module 220 and outputs a best (most likely)predicted road-agent trajectory based on its having a confidence score452 corresponding to the highest level of confidence among the predictedroad-agent trajectories generated by trajectory-prediction module 220.In that embodiment, control module 235 can plan a trajectory for vehicle100 based, at least in part, on the most likely predicted trajectory ofthe road agent. Executing the planned trajectory could, in some cases,involve control module 235 taking partial or fully autonomous control ofone or more vehicle systems 140 of vehicle 100, such as steering system143, braking system 142, or throttle system 144. For example, controlmodule 235 may determine that the most likely predicted trajectory of aroad agent would put vehicle 100 on a collision course with the roadagent but that an adjustment to the trajectory of vehicle 100 canprevent a collision. The planned trajectory can be executed when thevehicle is in a fully autonomous driving mode or when vehicle 100 is ina parallel-autonomy mode (e.g., when a human driver is driving thevehicle with an advanced driver-assistance system or “ADAS” engaged).

As explained above, in an embodiment with two trajectory predictors,control module 235 receives predicted road-agent trajectories andconfidence scores 452 output by trajectory-output module 230 andincludes instructions that cause the one or more processors 110 tocontrol the operation of vehicle 100 based, at least in part, on one ormore of (1) the first predicted road-agent trajectory; (2) the secondpredicted road-agent trajectory; (3) the confidence score for the firstpredicted road-agent trajectory; and (4) the confidence score for thesecond predicted road-agent trajectory. If more than two trajectorypredictors are used in trajectory-prediction module 220, this techniquegeneralizes to m trajectory predictors, where m is greater than 2. Also,a single variational trajectory predictor can generate more than onepredicted road-agent trajectory by taking multiple samples from aroad-agent trajectory distribution. As also explained above, controllingthe operation of vehicle 100 can, in some embodiments, include planninga trajectory for vehicle 100 (the ego vehicle) based, at least in part,on a most likely road-agent trajectory output by trajectory-outputmodule 230. The planned trajectory can then be executed in a fullyautonomous driving mode of vehicle 100 or in a parallel-autonomy mode(e.g., an ADAS), as explained above.

Referring again to FIG. 2, model-training module 240 causes the one ormore processors 110 to perform functions pertaining to the training ofthe DNNs employed, respectively, in variational trajectory predictor 420and confidence estimator 430. In some embodiments, those two DNN modelsare trained with different loss functions and regularization terms, asexplained below. In one particular embodiment, both models areimplemented using PyTorch and trained on an AWS server with four TeslaV100 graphics processing units (GPUs) operating in parallel.

In some embodiments, variational trajectory predictor 420 is trained asin variational inference, with the loss function defined to be thenegative log-likelihood of the ground-truth trajectory coefficientsgiven the GMM parameters 450 output by the model, in addition to severalregularization terms. As those skilled in the art are aware,“regularization terms” are those not dependent on data that giveguidance or direction to the choice of parameters the neural network isoutputting.

The log probability for a single Gaussian component with basisdimensionality D is computed as follows:

${{{LP}_{i}(c)} = {{\sum\limits_{d = 1}^{D}{- {\log\left( {2\pi\;\sigma_{d}^{2}} \right)}}} - \frac{\left( {c_{d} - \mu_{d}} \right)^{2}}{\sigma_{d}^{2}}}},$where the c_(d) are the individual projection coefficients of theapplicable trajectory. The mathematical symbols of the GMM parameters450 were identified above.

The negative log-likelihood for all mixture components is computed asfollows:

NLL ⁢ ( c ) = - log ⁢ ∑ i ⁢ w i ⁢ exp ⁢ ⁢ LP i ⁡ ( c ) .

In some embodiments, to ensure the output weights and variance valuesare reasonable, the following loss functions can be used: an L2 loss onweight summation, an L_(0.5) norm loss (sum of square roots) onindividual weights, and an L2 loss on standard deviations. The totalloss is a summation of individual losses, as described above.

In some embodiments, in training the confidence estimator 430, the lossfunction is defined as the L2 error between the predicted confidencescores computed using the coefficients output by the model and theactual confidence scores (i.e., confidence scores determined relative tothe actual trajectory taken by the road agent in the training data). Insome embodiments, the error is computed with respect to the averagepredicted direction of travel. In other embodiments, the average erroris computed over a set of samples. For example, in one embodiment,confidence estimator 430 samples from the distribution, computes theerror between that and the path the road agent actually took in thetraining data, and averages the error over a set of samples. In general,the confidence score can be represented by any loss metric well-definedover the variational predictor and the expert predictor(s). Oneillustrative choice is displacement error at the end (limit) of thepredictive temporal horizon (e.g., the difference between an actualtrajectory at the end of the predictive temporal horizon and thepredicted trajectory at the end of the predictive temporal horizon).Another illustrative choice is root-mean-squared-error (RMSE) along theentire trajectory. Both of these metrics are used, in some embodiments.

FIG. 9 is a flowchart of a method 900 of predicting a trajectory of aroad agent, in accordance with an illustrative embodiment of theinvention. Method 900 will be discussed from the perspective oftrajectory prediction system 170 in FIG. 2. While method 900 isdiscussed in combination with trajectory prediction system 170, itshould be appreciated that method 900 is not limited to beingimplemented within trajectory prediction system 170, but trajectoryprediction system 170 is instead one example of a system that mayimplement method 900.

At block 910, trajectory-prediction module 220 generates, based on aplurality of inputs that include measured past road-agent trajectoryinformation and information from one or more sensors in the sensorsystem 120 of vehicle 100, a first predicted road-agent trajectory usinga first trajectory predictor, as discussed above. As also explainedabove, in some embodiments this first trajectory predictor is aprobabilistic variational trajectory predictor 420 that includes a DNN.In other embodiments, the first trajectory predictor is a deterministictrajectory predictor (e.g., a physics-based model).

At block 920, trajectory-prediction module 220 generates, based on theplurality of inputs just mentioned, a second predicted road-agenttrajectory using a second trajectory predictor. As discussed above, insome embodiments the second trajectory predictor is a physics-based(deterministic) model.

At block 930, trajectory-prediction module 220 (specifically, confidenceestimator 430) generates a confidence score 452 for the first predictedroad-agent trajectory and a confidence score 452 for the secondpredicted road-agent trajectory. As explained above, confidenceestimator 430 includes a DNN, and the confidence scores 452 generatedfor the respective first and second predicted road-agent trajectoriesare computed as a continuous-time function within a predeterminedtemporal horizon of the predicted road-agent trajectories.

At block 940, trajectory-output module 230 outputs the first and secondpredicted road-agent trajectories and their respective confidence scoresreceived from trajectory-prediction module 220. That information can beused by other functional units of trajectory prediction system 170 suchas control module 235.

At block 950, control module 235 controls the operation of vehicle 100based, at least in part, on one or more of (1) the first predictedroad-agent trajectory; (2) the second predicted road-agent trajectory;(3) the confidence score for the first predicted road-agent trajectory;and (4) the confidence score for the second predicted road-agenttrajectory. As discussed above, this could, in some cases, involvecontrol module 235 taking partial or fully autonomous control of one ormore vehicle systems 140 of vehicle 100, such as steering system 143,braking system 142, or throttle system 144.

As discussed above, in one embodiment, trajectory-output module 230outputs a most likely predicted trajectory of the road agent byselecting the one of the first and second predicted road-agenttrajectories whose confidence score corresponds to a higher level ofconfidence. In that embodiment, control module 235 can plan a trajectoryfor vehicle 100 (the ego vehicle) based, at least in part, on that mostlikely predicted trajectory of the road agent. As discussed above, theplanned trajectory can then be executed in a fully autonomous drivingmode of vehicle 100 or in a parallel-autonomy mode (e.g., an ADAS).

FIG. 10 is a flowchart of a method 1000 of training the deep neuralnetwork of a variational trajectory predictor 420 and the deep neuralnetwork of a confidence estimator 430, in accordance with anillustrative embodiment of the invention. Method 1000 will be discussedfrom the perspective of trajectory prediction system 170 in FIG. 2.While method 1000 is discussed in combination with trajectory predictionsystem 170, it should be appreciated that method 1000 is not limited tobeing implemented within trajectory prediction system 170, buttrajectory prediction system 170 is instead one example of a system thatmay implement method 1000.

At block 1010, model-training module 240 trains the respective DNNs invariational trajectory predictor 420 and confidence estimator 430, asexplained above. In some embodiments, this training is performed usingdifferent loss functions and regularization terms for the two differentDNNs, as discussed above.

FIG. 1 will now be discussed in full detail as an example vehicleenvironment within which the system and methods disclosed herein mayoperate. In some instances, the vehicle 100 is configured to switchselectively between an autonomous mode, one or more semi-autonomousoperational modes, and/or a manual mode. Such switching also referred toas handover when transitioning to a manual mode can be implemented in asuitable manner, now known or later developed. “Manual mode” means thatall of or a majority of the navigation and/or maneuvering of the vehicleis performed according to inputs received from a user (e.g., humandriver/operator).

In one or more embodiments, the vehicle 100 is an autonomous vehicle. Asused herein, “autonomous vehicle” refers to a vehicle that operates inan autonomous mode. “Autonomous mode” refers to navigating and/ormaneuvering the vehicle 100 along a travel route using one or morecomputing systems to control the vehicle 100 with minimal or no inputfrom a human driver/operator. In one or more embodiments, the vehicle100 is highly automated or completely automated. In one embodiment, thevehicle 100 is configured with one or more semi-autonomous operationalmodes in which one or more computing systems perform a portion of thenavigation and/or maneuvering of the vehicle along a travel route, and avehicle operator (i.e., driver) provides inputs to the vehicle toperform a portion of the navigation and/or maneuvering of the vehicle100 along a travel route. Thus, in one or more embodiments, the vehicle100 operates autonomously according to a particular defined level ofautonomy. For example, the vehicle 100 can operate according to theSociety of Automotive Engineers (SAE) automated vehicle classifications0-5. In one embodiment, the vehicle 100 operates according to SAE level2, which provides for the autonomous driving module 160 controlling thevehicle 100 by braking, accelerating, and steering without operatorinput but the driver/operator is to monitor the driving and be vigilantand ready to intervene with controlling the vehicle 100 if theautonomous module 160 fails to properly respond or is otherwise unableto adequately control the vehicle 100.

The vehicle 100 can include one or more processors 110. In one or morearrangements, the processor(s) 110 can be a main processor of thevehicle 100. For instance, the processor(s) 110 can be an electroniccontrol unit (ECU). The vehicle 100 can include one or more data stores115 for storing one or more types of data. The data store 115 caninclude volatile and/or non-volatile memory. Examples of suitable datastores 115 include RAM (Random Access Memory), flash memory, ROM (ReadOnly Memory), PROM (Programmable Read-Only Memory), EPROM (ErasableProgrammable Read-Only Memory), EEPROM (Electrically ErasableProgrammable Read-Only Memory), registers, magnetic disks, opticaldisks, hard drives, or any other suitable storage medium, or anycombination thereof. The data store 115 can be a component of theprocessor(s) 110, or the data store 115 can be operably connected to theprocessor(s) 110 for use thereby. The term “operably connected,” as usedthroughout this description, can include direct or indirect connections,including connections without direct physical contact.

In one or more arrangements, the one or more data stores 115 can includemap data 116. The map data 116 can include maps of one or moregeographic areas. In some instances, the map data 116 can includeinformation or data on roads, traffic control devices, road markings,structures, features, and/or landmarks in the one or more geographicareas. The map data 116 can be in any suitable form. In some instances,the map data 116 can include aerial views of an area. In some instances,the map data 116 can include ground views of an area, including360-degree ground views. The map data 116 can include measurements,dimensions, distances, and/or information for one or more items includedin the map data 116 and/or relative to other items included in the mapdata 116. The map data 116 can include a digital map with informationabout road geometry. The map data 116 can be high quality and/or highlydetailed.

In one or more arrangement, the map data 116 can include one or moreterrain maps 117. The terrain map(s) 117 can include information aboutthe ground, terrain, roads, surfaces, and/or other features of one ormore geographic areas. The terrain map(s) 117 can include elevation datain the one or more geographic areas. The map data 116 can be highquality and/or highly detailed. The terrain map(s) 117 can define one ormore ground surfaces, which can include paved roads, unpaved roads,land, and other things that define a ground surface.

In one or more arrangement, the map data 116 can include one or morestatic obstacle maps 118. The static obstacle map(s) 118 can includeinformation about one or more static obstacles located within one ormore geographic areas. A “static obstacle” is a physical object whoseposition does not change or substantially change over a period of timeand/or whose size does not change or substantially change over a periodof time. Examples of static obstacles include trees, buildings, curbs,fences, railings, medians, utility poles, statues, monuments, signs,benches, furniture, mailboxes, large rocks, hills. The static obstaclescan be objects that extend above ground level. The one or more staticobstacles included in the static obstacle map(s) 118 can have locationdata, size data, dimension data, material data, and/or other dataassociated with it. The static obstacle map(s) 118 can includemeasurements, dimensions, distances, and/or information for one or morestatic obstacles. The static obstacle map(s) 118 can be high qualityand/or highly detailed. The static obstacle map(s) 118 can be updated toreflect changes within a mapped area.

The one or more data stores 115 can include sensor data 119. In thiscontext, “sensor data” means any information about the sensors that thevehicle 100 is equipped with, including the capabilities and otherinformation about such sensors. As will be explained below, the vehicle100 can include the sensor system 120. The sensor data 119 can relate toone or more sensors of the sensor system 120. As an example, in one ormore arrangements, the sensor data 119 can include information on one ormore LIDAR sensors 124 of the sensor system 120.

In some instances, at least a portion of the map data 116 and/or thesensor data 119 can be located in one or more data stores 115 locatedonboard the vehicle 100. Alternatively, or in addition, at least aportion of the map data 116 and/or the sensor data 119 can be located inone or more data stores 115 that are located remotely from the vehicle100.

As noted above, the vehicle 100 can include the sensor system 120. Thesensor system 120 can include one or more sensors. “Sensor” means anydevice, component and/or system that can detect, and/or sense something.The one or more sensors can be configured to detect, and/or sense inreal-time. As used herein, the term “real-time” means a level ofprocessing responsiveness that a user or system senses as sufficientlyimmediate for a particular process or determination to be made, or thatenables the processor to keep up with some external process.

In arrangements in which the sensor system 120 includes a plurality ofsensors, the sensors can function independently from each other.Alternatively, two or more of the sensors can work in combination witheach other. In such a case, the two or more sensors can form a sensornetwork. The sensor system 120 and/or the one or more sensors can beoperably connected to the processor(s) 110, the data store(s) 115,and/or another element of the vehicle 100 (including any of the elementsshown in FIG. 1). The sensor system 120 can acquire data of at least aportion of the external environment of the vehicle 100 (e.g., nearbyvehicles).

The sensor system 120 can include any suitable type of sensor. Variousexamples of different types of sensors will be described herein.However, it will be understood that the embodiments are not limited tothe particular sensors described. The sensor system 120 can include oneor more vehicle sensors 121. The vehicle sensor(s) 121 can detect,determine, and/or sense information about the vehicle 100 itself. In oneor more arrangements, the vehicle sensor(s) 121 can be configured todetect, and/or sense position and orientation changes of the vehicle100, such as, for example, based on inertial acceleration. In one ormore arrangements, the vehicle sensor(s) 121 can include one or moreaccelerometers, one or more gyroscopes, an inertial measurement unit(IMU), a dead-reckoning system, a global navigation satellite system(GNSS), a global positioning system (GPS), a navigation system 147,and/or other suitable sensors. The vehicle sensor(s) 121 can beconfigured to detect, and/or sense one or more characteristics of thevehicle 100. In one or more arrangements, the vehicle sensor(s) 121 caninclude a speedometer to determine a current speed of the vehicle 100.

Alternatively, or in addition, the sensor system 120 can include one ormore environment sensors 122 configured to acquire, and/or sense drivingenvironment data. “Driving environment data” includes and data orinformation about the external environment in which an autonomousvehicle is located or one or more portions thereof. For example, the oneor more environment sensors 122 can be configured to detect, quantifyand/or sense obstacles in at least a portion of the external environmentof the vehicle 100 and/or information/data about such obstacles. Suchobstacles may be stationary objects and/or dynamic objects. The one ormore environment sensors 122 can be configured to detect, measure,quantify and/or sense other things in the external environment of thevehicle 100, such as, for example, lane markers, signs, traffic lights,traffic signs, lane lines, crosswalks, curbs proximate the vehicle 100,off-road objects, etc.

Various examples of sensors of the sensor system 120 will be describedherein. The example sensors may be part of the one or more environmentsensors 122 and/or the one or more vehicle sensors 121. Moreover, thesensor system 120 can include operator sensors that function to track orotherwise monitor aspects related to the driver/operator of the vehicle100. However, it will be understood that the embodiments are not limitedto the particular sensors described.

As an example, in one or more arrangements, the sensor system 120 caninclude one or more radar sensors 123, one or more LIDAR sensors 124,one or more sonar sensors 125, and/or one or more cameras 126. In one ormore arrangements, the one or more cameras 126 can be high dynamic range(HDR) cameras, infrared (IR) cameras and so on. In one embodiment, thecameras 126 include one or more cameras disposed within a passengercompartment of the vehicle for performing eye-tracking on theoperator/driver in order to determine a gaze of the operator/driver, aneye track of the operator/driver, and so on.

The vehicle 100 can include an input system 130. An “input system”includes any device, component, system, element or arrangement or groupsthereof that enable information/data to be entered into a machine. Theinput system 130 can receive an input from a vehicle passenger (e.g. adriver or a passenger). The vehicle 100 can include an output system135. An “output system” includes any device, component, or arrangementor groups thereof that enable information/data to be presented to avehicle passenger (e.g. a person, a vehicle passenger, etc.).

The vehicle 100 can include one or more vehicle systems 140. Variousexamples of the one or more vehicle systems 140 are shown in FIG. 1.However, the vehicle 100 can include more, fewer, or different vehiclesystems. It should be appreciated that although particular vehiclesystems are separately defined, each or any of the systems or portionsthereof may be otherwise combined or segregated via hardware and/orsoftware within the vehicle 100. The vehicle 100 can include apropulsion system 141, a braking system 142, a steering system 143,throttle system 144, a transmission system 145, a signaling system 146,and/or a navigation system 147. Each of these systems can include one ormore devices, components, and/or combination thereof, now known or laterdeveloped.

The navigation system 147 can include one or more devices, sensors,applications, and/or combinations thereof, now known or later developed,configured to determine the geographic location of the vehicle 100and/or to determine a travel route for the vehicle 100. The navigationsystem 147 can include one or more mapping applications to determine atravel route for the vehicle 100. The navigation system 147 can includea global positioning system, a local positioning system or a geolocationsystem.

The processor(s) 110, the trajectory prediction system 170, and/or theautonomous driving module(s) 160 can be operably connected tocommunicate with the various vehicle systems 140 and/or individualcomponents thereof. For example, returning to FIG. 1, the processor(s)110 and/or the autonomous driving module(s) 160 can be in communicationto send and/or receive information from the various vehicle systems 140to control the movement, speed, maneuvering, heading, direction, etc. ofthe vehicle 100. The processor(s) 110, the trajectory prediction system170, and/or the autonomous driving module(s) 160 may control some or allof these vehicle systems 140 and, thus, may be partially or fullyautonomous.

The processor(s) 110, the trajectory prediction system 170, and/or theautonomous driving module(s) 160 can be operably connected tocommunicate with the various vehicle systems 140 and/or individualcomponents thereof. For example, returning to FIG. 1, the processor(s)110, the trajectory prediction system 170, and/or the autonomous drivingmodule(s) 160 can be in communication to send and/or receive informationfrom the various vehicle systems 140 to control the movement, speed,maneuvering, heading, direction, etc. of the vehicle 100. Theprocessor(s) 110, the trajectory prediction system 170, and/or theautonomous driving module(s) 160 may control some or all of thesevehicle systems 140.

The processor(s) 110, the trajectory prediction system 170, and/or theautonomous driving module(s) 160 may be operable to control thenavigation and/or maneuvering of the vehicle 100 by controlling one ormore of the vehicle systems 140 and/or components thereof. For instance,when operating in an autonomous mode, the processor(s) 110, thetrajectory prediction system 170, and/or the autonomous drivingmodule(s) 160 can control the direction and/or speed of the vehicle 100.The processor(s) 110, the trajectory prediction system 170, and/or theautonomous driving module(s) 160 can cause the vehicle 100 to accelerate(e.g., by increasing the supply of fuel provided to the engine),decelerate (e.g., by decreasing the supply of fuel to the engine and/orby applying brakes) and/or change direction (e.g., by turning the fronttwo wheels). As used herein, “cause” or “causing” means to make, force,compel, direct, command, instruct, and/or enable an event or action tooccur or at least be in a state where such event or action may occur,either in a direct or indirect manner.

The vehicle 100 can include one or more actuators 150. The actuators 150can be any element or combination of elements operable to modify, adjustand/or alter one or more of the vehicle systems 140 or componentsthereof responsive to receiving signals or other inputs from theprocessor(s) 110 and/or the autonomous driving module(s) 160. Anysuitable actuator can be used. For instance, the one or more actuators150 can include motors, pneumatic actuators, hydraulic pistons, relays,solenoids, and/or piezoelectric actuators, just to name a fewpossibilities.

The vehicle 100 can include one or more modules, at least some of whichare described herein. The modules can be implemented ascomputer-readable program code that, when executed by a processor 110,implement one or more of the various processes described herein. One ormore of the modules can be a component of the processor(s) 110, or oneor more of the modules can be executed on and/or distributed among otherprocessing systems to which the processor(s) 110 is operably connected.The modules can include instructions (e.g., program logic) executable byone or more processor(s) 110. Alternatively, or in addition, one or moredata store 115 may contain such instructions.

In one or more arrangements, one or more of the modules described hereincan include artificial or computational intelligence elements, e.g.,neural network, fuzzy logic or other machine learning algorithms.Further, in one or more arrangements, one or more of the modules can bedistributed among a plurality of the modules described herein. In one ormore arrangements, two or more of the modules described herein can becombined into a single module.

The vehicle 100 can include one or more autonomous driving modules 160.The autonomous driving module(s) 160 can be configured to receive datafrom the sensor system 120 and/or any other type of system capable ofcapturing information relating to the vehicle 100 and/or the externalenvironment of the vehicle 100. In one or more arrangements, theautonomous driving module(s) 160 can use such data to generate one ormore driving scene models. The autonomous driving module(s) 160 candetermine position and velocity of the vehicle 100. The autonomousdriving module(s) 160 can determine the location of obstacles, or otherenvironmental features including traffic signs, trees, shrubs,neighboring vehicles, pedestrians, etc.

The autonomous driving module(s) 160 can be configured to receive,and/or determine location information for obstacles within the externalenvironment of the vehicle 100 for use by the processor(s) 110, and/orone or more of the modules described herein to estimate position andorientation of the vehicle 100, vehicle position in global coordinatesbased on signals from a plurality of satellites, or any other dataand/or signals that could be used to determine the current state of thevehicle 100 or determine the position of the vehicle 100 with respect toits environment for use in either creating a map or determining theposition of the vehicle 100 in respect to map data.

The autonomous driving module(s) 160 either independently or incombination with the trajectory prediction system 170 can be configuredto determine travel path(s), current autonomous driving maneuvers forthe vehicle 100, future autonomous driving maneuvers and/ormodifications to current autonomous driving maneuvers based on dataacquired by the sensor system 120, driving scene models, and/or datafrom any other suitable source. “Driving maneuver” means one or moreactions that affect the movement of a vehicle. Examples of drivingmaneuvers include: accelerating, decelerating, braking, turning, movingin a lateral direction of the vehicle 100, changing travel lanes,merging into a travel lane, and/or reversing, just to name a fewpossibilities. The autonomous driving module(s) 160 can be configuredcan be configured to implement determined driving maneuvers. Theautonomous driving module(s) 160 can cause, directly or indirectly, suchautonomous driving maneuvers to be implemented. As used herein, “cause”or “causing” means to make, command, instruct, and/or enable an event oraction to occur or at least be in a state where such event or action mayoccur, either in a direct or indirect manner. The autonomous drivingmodule(s) 160 can be configured to execute various vehicle functionsand/or to transmit data to, receive data from, interact with, and/orcontrol the vehicle 100 or one or more systems thereof (e.g. one or moreof vehicle systems 140).

Detailed embodiments are disclosed herein. However, it is to beunderstood that the disclosed embodiments are intended only as examples.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a basis for theclaims and as a representative basis for teaching one skilled in the artto variously employ the aspects herein in virtually any appropriatelydetailed structure. Further, the terms and phrases used herein are notintended to be limiting but rather to provide an understandabledescription of possible implementations. Various embodiments are shownin FIGS. 1-10, but the embodiments are not limited to the illustratedstructure or application.

The flowcharts and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments. In this regard, each block in the flowcharts or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved.

The systems, components and/or processes described above can be realizedin hardware or a combination of hardware and software and can berealized in a centralized fashion in one processing system or in adistributed fashion where different elements are spread across severalinterconnected processing systems. Any kind of processing system oranother apparatus adapted for carrying out the methods described hereinis suited. A typical combination of hardware and software can be aprocessing system with computer-usable program code that, when beingloaded and executed, controls the processing system such that it carriesout the methods described herein. The systems, components and/orprocesses also can be embedded in a computer-readable storage, such as acomputer program product or other data programs storage device, readableby a machine, tangibly embodying a program of instructions executable bythe machine to perform methods and processes described herein. Theseelements also can be embedded in an application product which comprisesall the features enabling the implementation of the methods describedherein and, which when loaded in a processing system, is able to carryout these methods.

Furthermore, arrangements described herein may take the form of acomputer program product embodied in one or more computer-readable mediahaving computer-readable program code embodied, e.g., stored, thereon.Any combination of one or more computer-readable media may be utilized.The computer-readable medium may be a computer-readable signal medium ora computer-readable storage medium. The phrase “computer-readablestorage medium” means a non-transitory storage medium. Acomputer-readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer-readable storage medium would include the following: a portablecomputer diskette, a hard disk drive (HDD), a solid-state drive (SSD), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a portable compact disc read-only memory (CD-ROM), adigital versatile disc (DVD), an optical storage device, a magneticstorage device, or any suitable combination of the foregoing. In thecontext of this document, a computer-readable storage medium may be anytangible medium that can contain or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

Program code embodied on a computer-readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber, cable, RF, etc., or any suitable combination ofthe foregoing. Computer program code for carrying out operations foraspects of the present arrangements may be written in any combination ofone or more programming languages, including an object-orientedprogramming language such as Java™, Smalltalk, C++ or the like andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The program codemay execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer, or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider).

The terms “a” and “an,” as used herein, are defined as one or more thanone. The term “plurality,” as used herein, is defined as two or morethan two. The term “another,” as used herein, is defined as at least asecond or more. The terms “including” and/or “having,” as used herein,are defined as comprising (i.e. open language). The phrase “at least oneof . . . and . . . ” as used herein refers to and encompasses any andall possible combinations of one or more of the associated listed items.As an example, the phrase “at least one of A, B, and C” includes A only,B only, C only, or any combination thereof (e.g. AB, AC, BC or ABC).

Aspects herein can be embodied in other forms without departing from thespirit or essential attributes thereof. Accordingly, reference should bemade to the following claims rather than to the foregoing specification,as indicating the scope hereof.

What is claimed is:
 1. A system for predicting a trajectory of a roadagent, the system comprising: one or more processors; and a memorycommunicably coupled to the one or more processors and storing: atrajectory-prediction module including instructions that when executedby the one or more processors cause the one or more processors to:generate a first predicted road-agent trajectory using a firsttrajectory predictor, wherein the road agent is external to a vehicleand the first trajectory predictor generates the first predictedroad-agent trajectory based, at least in part, on a plurality of inputsthat include past road-agent trajectory information and information fromone or more sensors of the vehicle; generate a second predictedroad-agent trajectory using a second trajectory predictor, wherein thesecond trajectory predictor generates the second predicted road-agenttrajectory based, at least in part, on the plurality of inputs; andgenerate a confidence score for each of the first and second predictedroad-agent trajectories using a confidence estimator that includes afirst deep neural network, wherein the confidence scores are computed asa function of time within a predetermined temporal horizon; atrajectory-output module including instructions that when executed bythe one or more processors cause the one or more processors to outputthe first and second predicted road-agent trajectories and theirrespective confidence scores; and a control module includinginstructions that when executed by the one or more processors cause theone or more processors to control operation of the vehicle based, atleast in part, on one or more of the first predicted road-agenttrajectory, the second predicted road-agent trajectory, the confidencescore for the first predicted road-agent trajectory, and the confidencescore for the second predicted road-agent trajectory.
 2. The system ofclaim 1, wherein the road agent is one of an automobile, a motorcycle, abicycle, and a pedestrian.
 3. The system of claim 1, wherein the firsttrajectory predictor is a probabilistic variational trajectory predictorthat includes a second deep neural network.
 4. The system of claim 3,wherein the trajectory-prediction module includes instructions togenerate the first predicted road-agent trajectory, at least in part, bycomputing Gaussian mixture model (GMM) parameters for one or moreprojection coefficients of a basis function.
 5. The system of claim 4,wherein the GMM parameters include at least one of a weight, a mean, anda variance.
 6. The system of claim 3, further comprising: amodel-training module including instructions that when executed by theone or more processors cause the one or more processors to train thefirst and second deep neural networks.
 7. The system of claim 1, whereinthe past road-agent trajectory information includes a sequence ofspatial coordinates projected onto one or more coefficients of a basisfunction.
 8. The system of claim 1, wherein the information from one ormore sensors of the vehicle includes at least one of image data, lightdetection and ranging (LIDAR) data, radar data, and sonar data.
 9. Thesystem of claim 1, wherein the second trajectory predictor is aphysics-based model.
 10. The system of claim 1, wherein the first andsecond trajectory predictors use different computational models togenerate the respective first and second predicted road-agenttrajectories.
 11. The system of claim 1, wherein the trajectory-outputmodule includes further instructions to output, as a most likelypredicted trajectory of the road agent, the one of the first and secondpredicted road-agent trajectories whose confidence score corresponds toa higher level of confidence, and the control module includesinstructions to plan a trajectory for the vehicle based, at least inpart, on the most likely predicted trajectory of the road agent.
 12. Anon-transitory computer-readable medium for predicting a trajectory of aroad agent and storing instructions that when executed by one or moreprocessors cause the one or more processors to: generate a firstpredicted road-agent trajectory using a first trajectory predictor,wherein the road agent is external to a vehicle and the first trajectorypredictor generates the first predicted road-agent trajectory based, atleast in part, on a plurality of inputs that include past road-agenttrajectory information and information from one or more sensors of thevehicle; generate a second predicted road-agent trajectory using asecond trajectory predictor, wherein the second trajectory predictorgenerates the second predicted road-agent trajectory based, at least inpart, on the plurality of inputs; generate a confidence score for eachof the first and second predicted road-agent trajectories using aconfidence estimator that includes a first deep neural network, whereinthe confidence scores are computed as a function of time within apredetermined temporal horizon; output the first and second predictedroad-agent trajectories and their respective confidence scores; andcontrol operation of the vehicle based, at least in part, on one or moreof the first predicted road-agent trajectory, the second predictedroad-agent trajectory, the confidence score for the first predictedroad-agent trajectory, and the confidence score for the second predictedroad-agent trajectory.
 13. The non-transitory computer-readable mediumof claim 12, wherein: the first trajectory predictor is a probabilisticvariational trajectory predictor that includes a second deep neuralnetwork; the second trajectory predictor is a physics-based model; andthe instructions to generate the first predicted road-agent trajectoryinclude instructions to compute Gaussian mixture model (GMM) parametersfor one or more projection coefficients of a basis function, the GMMparameters including at least one of a weight, a mean, and a variance.14. A method of predicting a trajectory of a road agent, the methodcomprising: generating a first predicted road-agent trajectory using afirst trajectory predictor, wherein the road agent is external to avehicle and the first trajectory predictor generates the first predictedroad-agent trajectory based, at least in part, on a plurality of inputsthat include past road-agent trajectory information and information fromone or more sensors of the vehicle; generating a second predictedroad-agent trajectory using a second trajectory predictor, wherein thesecond trajectory predictor generates the second predicted road-agenttrajectory based, at least in part, on the plurality of inputs;generating a confidence score for each of the first and second predictedroad-agent trajectories using a confidence estimator that includes afirst deep neural network, wherein generating the confidence scoresincludes computing the confidence scores as a function of time within apredetermined temporal horizon; outputting the first and secondpredicted road-agent trajectories and their respective confidencescores; and controlling operation of the vehicle based, at least inpart, on one or more of the first predicted road-agent trajectory, thesecond predicted road-agent trajectory, the confidence score for thefirst predicted road-agent trajectory, and the confidence score for thesecond predicted road-agent trajectory.
 15. The method of claim 14,wherein the road agent is one of an automobile, a motorcycle, a bicycle,and a pedestrian.
 16. The method of claim 14, wherein the firsttrajectory predictor generates the first predicted road-agent trajectoryin accordance with a probabilistic variational model that includes asecond deep neural network.
 17. The method of claim 16, whereingenerating the first predicted road-agent trajectory includes computingGaussian mixture model (GMM) parameters for one or more projectioncoefficients of a basis function, the GMM parameters including at leastone of a weight, a mean, and a variance.
 18. The method of claim 16,further comprising training the first and second deep neural networks.19. The method of claim 14, wherein the first and second trajectorypredictors generate the respective first and second predicted road-agenttrajectories in accordance with different computational models.
 20. Themethod of claim 14, further comprising: outputting a most likelypredicted trajectory of the road agent by selecting the one of the firstand second predicted road-agent trajectories whose confidence scorecorresponds to a higher level of confidence; and planning a trajectoryfor the vehicle based, at least in part, on the most likely predictedtrajectory of the road agent.